IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v273y2020ics0306261920306632.html
   My bibliography  Save this article

The economic end of life of electrochemical energy storage

Author

Listed:
  • He, Guannan
  • Ciez, Rebecca
  • Moutis, Panayiotis
  • Kar, Soummya
  • Whitacre, Jay F.

Abstract

The useful life of electrochemical energy storage (EES) is a critical factor to system planning, operation, and economic assessment. Today, systems commonly assume a physical end-of-life criterion: EES systems are retired when their remaining capacity reaches a threshold below which the EES is of little use because of insufficient capacity and efficiency. We have found, however, that there are some instances where, while the EES is still functional, it is no longer economically profitable; we call this criterion the economic end of life of the system. This criterion depends on the use case and degradation characteristics of the EES. Using an intertemporal operational framework to consider functionality and profitability degradation, our case study shows that the economic end of life could occur significantly faster than the physical end of life. We argue that both criteria should be applied in EES system planning and assessment. We also analyze how R&D efforts should consider cycling capability and calendar degradation rate when considering the economic end of life of EES.

Suggested Citation

  • He, Guannan & Ciez, Rebecca & Moutis, Panayiotis & Kar, Soummya & Whitacre, Jay F., 2020. "The economic end of life of electrochemical energy storage," Applied Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:appene:v:273:y:2020:i:c:s0306261920306632
    DOI: 10.1016/j.apenergy.2020.115151
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920306632
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115151?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Song, Ziyou & Feng, Shuo & Zhang, Lei & Hu, Zunyan & Hu, Xiaosong & Yao, Rui, 2019. "Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Zhang, Yongzhi & Xiong, Rui & He, Hongwen & Qu, Xiaobo & Pecht, Michael, 2019. "State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O2 cells: Capacity loss modeling and remaining useful life prediction," Applied Energy, Elsevier, vol. 255(C).
    4. William A. Braff & Joshua M. Mueller & Jessika E. Trancik, 2016. "Value of storage technologies for wind and solar energy," Nature Climate Change, Nature, vol. 6(10), pages 964-969, October.
    5. Shafiee, Soroush & Zamani-Dehkordi, Payam & Zareipour, Hamidreza & Knight, Andrew M., 2016. "Economic assessment of a price-maker energy storage facility in the Alberta electricity market," Energy, Elsevier, vol. 111(C), pages 537-547.
    6. Patil, Meru A. & Tagade, Piyush & Hariharan, Krishnan S. & Kolake, Subramanya M. & Song, Taewon & Yeo, Taejung & Doo, Seokgwang, 2015. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Elsevier, vol. 159(C), pages 285-297.
    7. Hu, Chao & Jain, Gaurav & Tamirisa, Prabhakar & Gorka, Tom, 2014. "Method for estimating capacity and predicting remaining useful life of lithium-ion battery," Applied Energy, Elsevier, vol. 126(C), pages 182-189.
    8. Robert L. Fares & Michael E. Webber, 2017. "The impacts of storing solar energy in the home to reduce reliance on the utility," Nature Energy, Nature, vol. 2(2), pages 1-10, February.
    9. Jun Lu & Tianpin Wu & Khalil Amine, 2017. "State-of-the-art characterization techniques for advanced lithium-ion batteries," Nature Energy, Nature, vol. 2(3), pages 1-13, March.
    10. Noah Kittner & Felix Lill & Daniel M. Kammen, 2017. "Energy storage deployment and innovation for the clean energy transition," Nature Energy, Nature, vol. 2(9), pages 1-6, September.
    11. Paul R. Shearing, 2016. "Batteries: Imaging degradation," Nature Energy, Nature, vol. 1(11), pages 1-2, November.
    12. Guannan He & Qixin Chen & Panayiotis Moutis & Soummya Kar & Jay F. Whitacre, 2018. "An intertemporal decision framework for electrochemical energy storage management," Nature Energy, Nature, vol. 3(5), pages 404-412, May.
    13. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    14. Chang, Yang & Fang, Huajing & Zhang, Yong, 2017. "A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery," Applied Energy, Elsevier, vol. 206(C), pages 1564-1578.
    15. A. Stephan & B. Battke & M. D. Beuse & J. H. Clausdeinken & T. S. Schmidt, 2016. "Limiting the public cost of stationary battery deployment by combining applications," Nature Energy, Nature, vol. 1(7), pages 1-9, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. María Blecua-de-Pedro & Maryori C. Díaz-Ramírez, 2021. "Assessment of Potential Barriers to the Implementation of an Innovative AB-FB Energy Storage System under a Sustainable Perspective," Sustainability, MDPI, vol. 13(19), pages 1-16, October.
    2. Shin, Wooyoung & Lee, Choongman & Chung, In-Young & Lim, Jingon & Youn, Juyoung & Rhie, Younghoon & Hur, Kyeon & Shim, Jae Woong, 2022. "Reserve replacement from governor to energy storage system on conventional generator for operating-cost reduction," Applied Energy, Elsevier, vol. 324(C).
    3. Wu, Di & Ma, Xu & Balducci, Patrick & Bhatnagar, Dhruv, 2021. "An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands," Applied Energy, Elsevier, vol. 286(C).
    4. Ashwani Kumar Malviya & Mehdi Zarehparast Malekzadeh & Francisco Enrique Santarremigia & Gemma Dolores Molero & Ignacio Villalba-Sanchis & Victor Yepes, 2024. "A Formulation Model for Computations to Estimate the Lifecycle Cost of NiZn Batteries," Sustainability, MDPI, vol. 16(5), pages 1-22, February.
    5. Collath, Nils & Cornejo, Martin & Engwerth, Veronika & Hesse, Holger & Jossen, Andreas, 2023. "Increasing the lifetime profitability of battery energy storage systems through aging aware operation," Applied Energy, Elsevier, vol. 348(C).
    6. Chen, Xinjiang & Yang, Yu & Wang, Jianxiao & Song, Jie & He, Guannan, 2023. "Battery valuation and management for battery swapping station," Energy, Elsevier, vol. 279(C).
    7. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    8. Schleifer, Anna H. & Murphy, Caitlin A. & Cole, Wesley J. & Denholm, Paul, 2022. "Exploring the design space of PV-plus-battery system configurations under evolving grid conditions," Applied Energy, Elsevier, vol. 308(C).
    9. Fangyuan Qian & Shuiye Niu & Yujuan Xi, 2022. "Multi-Technology Driven R&D Cost Improvement Scheme and Application Utility of EESP in Energy-Intensive Manufacturing Industry," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    10. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    11. Vykhodtsev, Anton V. & Jang, Darren & Wang, Qianpu & Rosehart, William & Zareipour, Hamidreza, 2022. "A review of modelling approaches to characterize lithium-ion battery energy storage systems in techno-economic analyses of power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Müller, Simon C. & Welpe, Isabell M., 2018. "Sharing electricity storage at the community level: An empirical analysis of potential business models and barriers," Energy Policy, Elsevier, vol. 118(C), pages 492-503.
    2. Chen, Xinjiang & Yang, Yu & Wang, Jianxiao & Song, Jie & He, Guannan, 2023. "Battery valuation and management for battery swapping station," Energy, Elsevier, vol. 279(C).
    3. Mathews, Ian & Xu, Bolun & He, Wei & Barreto, Vanessa & Buonassisi, Tonio & Peters, Ian Marius, 2020. "Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging," Applied Energy, Elsevier, vol. 269(C).
    4. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    5. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Ma, Junpeng & Luo, Guangzhao & Teodorescu, Remus, 2020. "An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system," Energy, Elsevier, vol. 206(C).
    6. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
    7. Shan, Rui & Abdulla, Ahmed & Li, Mingquan, 2021. "Deleterious effects of strategic, profit-seeking energy storage operation on electric power system costs," Applied Energy, Elsevier, vol. 292(C).
    8. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(C).
    10. Lidang Jiang & Qingsong Huang & Ge He, 2024. "Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network," Energies, MDPI, vol. 17(7), pages 1-20, April.
    11. Gorman, Will & Montañés, Cristina Crespo & Mills, Andrew & Kim, James Hyungkwan & Millstein, Dev & Wiser, Ryan, 2022. "Are coupled renewable-battery power plants more valuable than independently sited installations?," Energy Economics, Elsevier, vol. 107(C).
    12. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings," Applied Energy, Elsevier, vol. 228(C), pages 2165-2175.
    13. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Cadini, F. & Sbarufatti, C. & Cancelliere, F. & Giglio, M., 2019. "State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters," Applied Energy, Elsevier, vol. 235(C), pages 661-672.
    15. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    16. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. Qin, Chao (Chris) & Loth, Eric, 2021. "Isothermal compressed wind energy storage using abandoned oil/gas wells or coal mines," Applied Energy, Elsevier, vol. 292(C).
    18. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    19. Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
    20. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:273:y:2020:i:c:s0306261920306632. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.